Unstructured

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Unstructured AI

Claude is genuinely bad at one thing i need most, and i've built my whole workflow around routing past it

posting this as craft, not complaint, because working out where a tool is weak is more useful than pretending it's uniformly great. the weakness, for me: it cannot reliably tell me when something is boring. it can tell me when writing is unclear, unstructured, grammatically off, poorly argued. it will fix all of that beautifully. but "this is competent and lifeless and nobody will finish reading it" is a judgment it makes badly, because boring is not a property of the text, it's a property of the reader's attention, and it has never been bored in its life. so it will happily polish a dead paragraph into a well-structured dead paragraph, and tell me it's improved, and technically it has, and the thing is still dead. what i do about it. i never ask "is this good." i ask mechanical questions it can actually answer: "where does the argument repeat itself," "which sentence is doing the least work," "what would a skeptical reader push back on." those are structural, and it's excellent at structural. and then the aliveness question stays mine. i read it out loud. i notice where i skim my own writing, because if i skim it, everyone will. that judgment doesn't get outsourced, and honestly i think trying to outsource it is how people end up publishing the polished lifeless stuff that's flooding everything right now. knowing exactly where the tool stops being useful is what lets me lean on it hard everywhere else. submitted by /u/Big_Currency_1805 [link] [comments]

How My Friend Made His First $70K Selling Websites

My web designer friend from California is passionate about building websites, and he wanted to make a full time business out of it. We talked a lot, and I gave him a lot of advice and stuff he could do to scale his web agency. He used to cold call, get a few clients, and run paid ads, get a few clients, but the cost of ads would just make him no profit. Cold calling was also tiring, and he couldn't keep it up while doing all the other stuff. So he wanted a real system, a blueprint he could follow every day. This is exactly how my friend scaled his web design company. Copy it if you feel stuck and don't know where to find your next project. ➜ Run 2 types of email automation targeting businesses without websites and businesses with websites. ➜ 1. For businesses without websites: scrape businesses with no websites, set up a sequence, and add 3–5 follow-ups. They either block you or you land a project. ➜ 2. For businesses with websites: scrape businesses with websites, analyze each business website, and turn flaws in outdated design, unstructured layout, no mobile optimization, and SEO issues into ready to send outreach emails with 3–5 follow ups. You can do both types of outreach in a tool called Swokei. ➜ 3. Have everything in one place: your leads, CRM, inbox, and calendar. You can also have that in Swokei. ➜ 4. Focus on SEO because it compounds over time. Fix your technical site SEO, and also blog or make content with high-intent keywords. Use a tool called Soro. ➜ 5. Host websites on a tool called Hetzner. It's very cheap and reliable, and you don't need to keep switching hosting platforms. Everything in one place. This is the whole workflow: automation in the background that lands you clients while you focus on building websites. Replies, meetings booked, CRM, everything in one place. With all that being said, he ended up buying a Mercedes-Benz with the $70k he made. 😂 That's not something I'd recommend, though. I'd personally reinvest it into the business or put it into stocks. submitted by /u/Murky_Explanation_73 [link] [comments]

AI as a co-production assistant on a philosophy project.

So I've been using Claude Cowork to help bring together a philosophical project I've been working on. It has been an invaluable tool - helping me refine and crystallise my understanding, represented in part across hundreds of documents, as is helping me consolidate everything into a work that can communicate its ideas to others. It has helped me ingest ideas across hundreds of documents, has a permanent memory architecture and session onbarding, structured reasoning files so that the same nuanced understanding is consistently developed in every session, and 13 different ai-author roles, including serving as interlocutor to help develop and refine ideas, or surfacing ideas latent within the written work, identifying my methodological intention, refining it into a methodological principles, and then serving as structural editor to ensure it is applied consistently throughout. The book project. How to Win a Revolution is a roughly 200,000-word philosophical book built around a diagnosis of the contemporary Western cultural crisis: not merely that people disagree, but that the concepts we disagree through (progress, freedom, truth, the self) have lost shared meaning, so argument across worldviews fails structurally. The book's response is not another argument. It is written instead using examples to guide the reader towards an understanding, taking the reader on a journey through the history of science, showing that physics faced a structurally similar crisis of irreconcilable concepts (wave versus particle) just before the quantum revolution. The book shows that we have lived through a similar time of crisis, an increase in cultural complexity, emerging and evolving patterns over time, and by guiding the reader through the reasoning, the methodological intention of the book is to demonstrate that implict within all manifest phenomena, there is history, an underlying process out of which they emerge, and once you understand the process you see project its future trajectory - I use obvious examples such as recent climate change, and recent history to demonstrate this; e.g. evolving social process online, emergent effects in culture, and moving through science, understanding, self, reality - to show that a similar pattern recurs across different order of complexity, across different domains - the books intention is to inspire an emergent comprehension in the reader. After that, it should be easy to recognise history as a process, culture as a static system that resists attempts at progress, and present a vision of an evolutionary socioeconomic system that moves beyond static ideologies, that can meet the underlying needs of left/right, which should then be easy to accept. The book derives, entirely from the familiar experience of history, evidence of contemporary science and its history, a philosophy from first principles rather than any existing philosophical tradition, an account of how understanding itself forms and progresses, in terms of evolving complex adaptive systems and emergent orders of complexity, and an underlying recursive process out of which it forms, out of which a self-consciousness emerges. The book's structure is recursive and self-enacting: evidence, reasoning, then explanatory inference at each stage, so the reader undergoes the very process of understanding change that the book describes, arriving at understanding on their own authority rather than being argued into agreement. The title is literal: the book is designed to produce, in miniature, the kind of revolution in comprehension it analyses. Why it is challenging. The book requires a carefully enacted, consistently applied and evolving methodology: the whole point is that reading should take little effort from the reader, with the complexity built into the design, rather than imposed upon the reader; the point is that it only works if it is non-partisan and accessible to any reader. This is a demanding task for an individual philosopher whose understanding comes from experience rather than academic tradition, and whose work is necessarily inconsistent with that tradition. It also means that there is no institutional support for such a project. Claude co-work has been a massive help; helping me build a philosophical system from my work, and memory arhcitecture that must hold a single recursive chain of reasoning coherent across 200,000 words and multiple domains (history of science, epistemology, cognition, culture). Its method compounds the difficulty: it must speak to a general reader of any political or religious starting point, remain non-partisan by design, meet an Einstein-grade evidential standard of logically coherent explanation, and be structured so the reader discovers the conclusions rather than being told them. Structure, tone, register, and cross-book consistency all carry philosophical load, so ordinary editorial slippage becomes a failure of the thesis itself. How AI helps. Over roughly thirty sessions the AI act

tested it in a language I don't speak, still got back a prompt like a spec

Built with a mix of Claude and Codex — figured I'd share the actual output here rather than the build story, since that's probably more interesting to this sub. It's a Windows voice app — hit a hotkey, talk normally, get a structured prompt out. To push it past languages I actually speak, I generated a Turkish voice clip with ElevenLabs (I don't speak Turkish, so this was the only honest way to stress-test it) and played it into the app through mic input — fully unstructured, rambling the way you'd explain a bug to a friend: [VIDEO: Turkish audio → structured English prompt output] No translating in your head first, no typing. It held up better than I expected — took the rambling and still came back with a clean, structured spec-style prompt. Context-aware too same hotkey, different output shape depending on what's focused. Code editor gets a ticket-style prompt, Gmail gets an email, Slack gets something short. Curious from people building real products with AI coding tools: for me it wasn't really the code itself — it was two other things eating most of my time. One was the packaging/distribution side (Store certification, OAuth, all of that). The other was the system prompt itself — endless iteration trying to get consistent output that actually reads well instead of something that just technically works. Both took way more time than I expected going in. Anyone else find that the "real" work starts after the code runs? If anyone wants to try it: https://apps.microsoft.com/detail/9MV0Q1F26DK6 submitted by /u/Emergency-Jelly-3543 [link] [comments]

Megathread Summary: I Asked Multiple Reddit Communities How to Build a Living Memory /Context Engine for Business. Here's what everyone had to say.

I am trying to build a living memory/context engine for my business, something that can remember projects, decisions, timelines, risks, and conversations across emails, documents, notes, chats, and meetings. Since this is new territory for me, I asked several Reddit communities for advice. The responses were incredibly thoughtful, and many people shared architectures, engineering trade-offs, tools, and lessons learned from building similar systems. I consolidated the best ideas into a single summary. If you're exploring the same problem, especially if you're just getting started like me, I hope this will help. Core Philosophies & Perspectives Query-First Design: Do not build the storage layer first. Write out 20 real-world queries you will ask tomorrow and architect backward, because the retrieval interface shapes the system more than the storage layer. Chief of Staff vs. Search Engine: The goal is not just retrieving raw data, but synthesis. Like Microsoft Clarity’s bulk insights, the system should process updates and proactively tell you what projects need attention, what changed, and what the blockers are. The "Daily Mirror" Briefing: Focus on what the user needs to know at the start of the next session to continue without context loss, rather than striving for perfect archival completeness. Four Separate Problems: Treating user queries as a single search issue will fail; "latest status" is a retrieval problem, "unresolved issues" is state tracking, "decisions made" is entity extraction, and "important updates" requires significance scoring. Architecture & Strategies Append-Only Event Logs First: Avoid starting with a massive knowledge graph or vector database. Ingest everything as a timestamped, append-only event log, and build the knowledge graph later as a derived query layer on top. Artifact-Mediated Continuity: To prevent identity collapse over long timelines, separate retrieval (facts) from reconstruction (identity and working context). Use a "Principal-owned Artifact System" with files like MEMORY.md for project state, "Texture Packs" for behavior descriptions, and "Lane Files" structured around the Five W's. Parallel Retrieval Paths: Pure vector search fails at scale. Run vector search (for semantic similarity) alongside a graph/relational lookup (for exact entities) in parallel, because neither covers the query surface alone. Hybrid search (semantic + BM25 keyword) is heavily recommended. Split Memory by Lifespan & Namespace: Sector your memory from day one. Split durable facts (stable preferences, user info) from working context (recent events), applying different decay rates and routing queries to the appropriate layer. Continuous Summarization: Instead of treating everything as unstructured documents, use an LLM pipeline to continuously extract structured facts from new inputs to update project briefs, decision logs, and risk trackers automatically. The Hardest Engineering Challenges Entity Resolution (The Silent Killer): Different sources will refer to the same thing differently (e.g., "Project X" vs "the X pilot"). Without an entity registry mapping aliases to canonical IDs before writing, your graph will become a mess of duplicates. Ontology & Classification: The hardest part is often getting the system to universally understand the difference between a "decision", a "discussion", or a "risk" across varying data structures like emails versus meeting transcripts. Temporal Relevance & Stale Context: A "decision" stays load-bearing for months, whereas a "status update" decays in days. If you don't encode decay rates and version records, stale facts will outrank fresh ones and confidently contradict recent updates. Significance Scoring: Standard retrieval returns everything recent, not everything important. Write-time scoring fails because significance is retrospective; a better approach is "adaptive salience," where chunks gain weight when retrieved and decay when ignored. Context Moodiness: Especially in greenfield projects, meaningful status updates can be muddied by confounding, irrelevant, or noisy data. Tools & Tech Stack Recommendations Storage / Databases: Vector stores like pgvector for semantic search, paired with key-value or relational databases for exact lookup. Airtable, Databricks, Notion, and Obsidian were also noted as strong foundational or single-source-of-truth layers. AI Models & Agents: Claude Code, OpenAI Codex, Hermes-agent (by Nous Research), AsanaAI, and ClickUp Brain. Injecting local LLMs where appropriate can help cut down on continuous API costs. Middleware & Pipelines: Kapex: Memory middleware built specifically to score node significance, governing lifecycle so resolved stuff fades and unresolved stuff persists. Sauna.ai: An engine built out of Wordware that fits this use case. Automation: Make.com or n8n for routing deterministic logic and LLM reasoning. The "Party Model": A CRM data integration framework

Using Claude as a deterministic metric engine via Postgres queues. Anyone doing this?

I've been working on turning unstructured field data into calibrated metrics. Instead of normal RAG, I built a system where AI agents act as a metric engine. Architecture: - Unstructured data goes into Postgres. - Queue system (SELECT FOR UPDATE SKIP LOCKED) feeds it to Claude (Haiku/Sonnet). - Claude outputs deterministic JSON metrics. - Supabase RLS handles the multi-tenant isolation. It works incredibly well for scoring things objectively. Has anyone else built AI pipelines specifically for metric generation rather than chatbots? What edge cases should I watch out for?' submitted by /u/bestekarx [link] [comments]

Open image generation models are closer to closed-source quality than this sub thinks [D]

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different architectures. The consensus here seems to be that open models are still a generation behind closed APIs. Based on my recent benchmarks, that gap is way smaller than people assume. On compositional control specifically, the latest open checkpoints handle multi-object scenes with spatial relationships about as reliably as the paid endpoints I've tested. Not perfect, but close enough that the failure modes are comparable. The thing that surprised me was text rendering in images, which used to be a disaster on open models. Recent architectures actually get it right roughly 70-80% of the time on short strings. Generation speed is another misconception. People complain about inference time but I'm getting 2MP outputs in under two minutes on a single consumer GPU. Drop resolution and step count and you're at 30 seconds. Fine for iteration. The structured prompting argument also falls flat. Everyone acts like having explicit scene control is a downside when it's literally what production pipelines need. Unstructured text prompts are the hack, not the other way around. These models ship without community optimizations, no fine-tuning, no custom pipelines. The baseline is already competitive. submitted by /u/ProfessionalAnt7436 [link] [comments]

RAG for you see it live open source files any kind

This is for visualizing file extraction through RAG (or file ingestion into any structured data set). I've been really into different shapes of data (like graph db, etc). Today I decided to make a knowledge base out of many years of random phone notes, but it needed tons of enrichment, interpretation, and sort of translation to get scratch notes into anything you could call "meaningful" bullet points. I got really lost at some point and couldn't dig back out. Claudio kept changing the extraction steps on every batch I gave then redoing everything from the top. It was kindve a nightmare, so I decided to make a visualizer to try to standardize the process and stop burning cash on the api next time I need to make a rag. Live: https://whatsorag.vercel.app Code: https://github.com/Mx3RnD/whatsorag You can play around with the vercel - no signup no fee. It works very much like the Oai agent builder, but you work from both ends (from the left begining with multimodal files). unstructured in, structured out, RAG agent native. You can also pull up templates for all the good codebases like rag anything, lightrag, lazy graph rag, hippograph, RAPTOR, and colPali then visualize what those look like in a flow chart. For me this is much easier to understand and manipulate. the export will just give you the md for exactly what you see on screen which you can paste into claude code or codex, then point it at a folder of files. submitted by /u/Expensive-Hope-4727 [link] [comments]

RAG visualizer open source

This is for visualizing file extraction through RAG (or file ingestion into any structured data set). I've been really into different shapes of data (like graph db, etc). Today I decided to make a knowledge base out of many years of random phone notes, but it needed tons of enrichment, interpretation, and sort of translation to get scratch notes into anything you could call "meaningful" bullet points. I got really lost at some point and couldn't dig back out. Claudio kept changing the extraction steps on every batch I gave then redoing everything from the top. It was kindve a nightmare, so I decided to make a visualizer to try to standardize the process and stop burning cash on the api next time I need to make a rag. Live: https://whatsorag.vercel.app Code: https://github.com/Mx3RnD/whatsorag You can play around with the vercel - no signup no fee. It works very much like the Oai agent builder, but you work from both ends (from the left begining with multimodal files). unstructured in, structured out, RAG agent native. You can also pull up templates for all the good codebases like rag anything, lightrag, lazy graph rag, hippograph, RAPTOR, and colPali then visualize what those look like in a flow chart. For me this is much easier to understand and manipulate. the export will just give you the md for exactly what you see on screen which you can paste into claude code or codex, then point it at a folder of files. submitted by /u/Expensive-Hope-4727 [link] [comments]

I built a local PDF-to-Markdown converter so you don't have to burn LLM tokens.

If you're dumping raw PDFs into Claude or ChatGPT, you're wasting tokens and money. I built LiteDoc to fix this. It’s a 100% client-side tool that processes PDFs locally in your browser. LiteDoc A 100% Local, Browser-Based PDF to Markdown Converter (No Python, No pip install, No servers). What it does: Unpacks PDFs in memory without servers. Extracts text, isolates embedded images, and structures everything into clean Markdown. Handles LaTeX math and right-to-left Arabic natively. Detects custom-encoded "gibberish" fonts. If the text layer is corrupted, it automatically renders those specific pages or text bands as images. Outputs a .md file and an optimized image folder packed in a ZIP. You can try it here: litedoc.xyz github repo The Markdown Outcome ## Page 1 # Deep Structural Neural Mapping Deep learning strategies often fail when executing unstructured inputs directly. The loss function is defined as: $$L(\theta) = -\frac{1}{N}\sum_{i=1}^{N} \left[ y_i \log(\hat{y}_i) + (1-y_i)\log(1-\hat{y}_i) \right]$$ ## Page 2 [IMAGE: academic_paper_p2_img1.jpg] ### Arabic Sample Markdown إلى صيغة PDF هذا التطبيق أداةً مجانيةً لتحويل ملفات What's Behind It It runs on PDF.js and JSZip entirely in the browser. The extraction engine uses X-gap aware smart word joining to prevent broken sentences, detects column splits mathematically, and maps font sizes to Markdown heading levels (H1/H2/H3). It also fingerprints and strips repeating headers and footers. If it detects incompatible Unicode script mixing (which indicates a private font encoding), it aborts text extraction for that font and drops back to canvas-based image rendering. How It Saves Tokens LLMs charge heavily for vision and PDF rasterization (roughly 850 tokens per page). By processing the document locally, LiteDoc bypasses the AI's internal rasterizer. It extracts the raw text and recompresses embedded images to low/medium resolutions. Instead of uploading a heavy 50-page PDF, you paste the raw text and only the specific images you need. You drop your token usage from tens of thousands of tokens down to the raw character count. edit: What's New in v2.0 (Just Released): XY-Cut DLA Engine: Replaced blind linear reading with a recursive algorithm that geometrically maps pages, isolating headers, sidebars, and main text blocks. Asymmetrical Multi-Column Routing: Natively processes columns top-to-bottom without horizontal text interleaving. Vector-Based Table Reconstruction: Captures table structures as clean Markdown grids, bypassing OCR. Heavy-Duty Memory Management: Processes files in 10-page chunks and forcefully clears VRAM to prevent browser crashes on 200+ page docs. Language Auto-Detect: Runs a lightweight pre-pass to detect script before initializing heavy language workers. Test it out, break it, and drop an issue on GitHub if you find a bug. If it saves you API costs, star the repo. litedoc.xyz | GitHub https://preview.redd.it/xy5j9mwfwi6h1.png?width=1200&format=png&auto=webp&s=144aaa22c208cf16587b628c6d207a9e5526fc51 submitted by /u/mxsus [link] [comments]

Would you say capture-time semantic annotation for robot trajectories is a solved problem? [R]

It seems raw teleoperation data (RGB + joint states) structurally lacks affordance, contact intent, and embodiment-specific kinematic context. (information that can't be reliably recovered post-hoc once the demonstration is recorded) Most current approaches either filter/clean after collection, or rely on simulation to compensate. But neither seems to close the semantic gap for contact-rich tasks in unstructured environments. Is anyone working on supervision at acquisition time, enriching the stream as it's captured rather than labeling after the fact? And if not, is this a real bottleneck or am I overestimating the problem? submitted by /u/Several-Many9101 [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

HELP !!

I have 30+mb pdfs of unstructured and unorganzied data in form of pdf which includes screenshots, notes, handwritten notes and some images. I'm looking for any website or method , where I can convert my pdfs into organized and structured html/csv with almost full and most accuracy without skipping anything so it may interact with the claude later on smoothly. I liked "thepi.pe" but it was little expensive for me plus it has pdf size limit too. what should I do ??? pls guide me. I wanna extract exact data in organzized and structured form preferably with a customized prompt. I will buy claude pro and I have huge pdfs which I'm avoiding to put directly on claude, I wanna do PYQs analysis and notes generation while sharing my own notes submitted by /u/InternalConnection95 [link] [comments]

Anthropic just confirmed why 90% of non-coding AI agents fail in production

Anthropic recently published an incredibly deep breakdown analyzing millions of real human-agent tool calls across their public API, and they shared a breakdown of where these agents are being deployed. They said “Software engineering makes up roughly 50% of all agentic activity on their platform”. Everything else: sales, marketing, finance, legal is sitting down in the single digits. A lot of the initial commentary around this has been along the lines of: "Oh, look, AI agents only work for coding. They haven't cracked the rest of the enterprise yet." But if you’ve tried to build and deploy an autonomous agent in a non-coding environment, you know that is the wrong conclusion. The models are more than capable but the real problem is that software engineering data is clean, while real-world business data is a horrific and unorganized. Think about it: Why Coding is Easy for Agents: Code lives in structured Git repo. It follows strict syntax rules, has clear docs and runs inside deterministic terminals. If an agent breaks something, the compiler throws a clean error message telling it exactly what went wrong. Why the Rest of the World is Hard: A sales or marketing agent doesn’t get a clean github repo instead you’re constantly dealing with changing information like competitor pricing and badly formatted data. When a non-coding agent fails, it’s almost never because the model lost its ability to reason but cause it gets choked out by unstructured web data that fills up its context window with thousands of useless tags and tracking scripts until it hallucinates. The developers getting agents to work in those low-percentage brackets on Anthropic's chart (like automated market research or live CRM routing) are usually spending most of their time on the boring infra work behind the scenes such as clean inputs, reliable scraping and that’s the part that really makes the difference. If you look at a modern, high-reliability agent stack outside of coding, it usually relies on three things: The Core Reasoner: Something fast with a massive context window like Claude Sonnet to handle the logic. Data Hygiene at the Gateway: Instead of letting the agent scrape raw web URLs directly (which triggers bot blocks and inputs HTML that will need to be revised), developers feed the internet data through dedicated markdown converters with tools like Firecrawl or Jina Reader are pretty standard here and the agent gets pure text, saving token costs and preventing hallucinations. The Guardrail Layer: Traditional code hooks or rules engines that check the agent’s output before it executes an irreversible action (like sending an email or updating a database record). The low adoption numbers in the rest of the enterprise doesn’t mean agents are overhyped. In most industries, the surrounding tooling just still kind of sucks so once the data side gets more reliable, you’ll probably see adoption spread a lot faster outside engineering What are your thoughts on this? For those building agents in finance, marketing, or operations, I would love to get your thoughts here! submitted by /u/Loud-Campaign-6312 [link] [comments]

I called this a few months ago - enterprises are burning unsustainable amounts on Claude, and now it's showing up in the news

A while back I wrote a post on r/wallstreetbets about why Anthropic's revenue story doesn't hold up the way the headlines suggest. It got removed because you can't take positions in a private company. But the core argument is playing out now, so I want to share it here for discussion. URL of the removed post: https://www.reddit.com/r/wallstreetbets/comments/1sxdjt5/if_anthropic_goes_public_this_year_its_gonna_be The thesis was simple: From my circles in tech scene in Berlin, enterprises are throwing Claude access at thousands of employees with zero training, zero budget controls, and zero accountability. It's not productivity - it's unstructured R&D at $100-200/person/month. Some examples I was hearing from people in my network working at large tech companies: Spending $70 on Opus to build a simple IF/ELSE formula in Google Sheets Dumping half a database into context trying to get "insights" Multiple people independently building internal tools that could've been a 10-line script Using Claude as a hobby project builder on company credits Multiply $150/person/month by 2,000-20,000 employees and you get $300K-$3M/month per company. That's not a defensible line item when the CFO eventually asks what the ROI is. The Uber and Microsoft stories are exactly what I expected. Budgets get set, access gets handed out broadly, then someone looks at the bill four months in and panics. This doesn't mean Claude is a bad product - it's genuinely the best model out there for a lot of tasks. But the enterprise revenue being cited in IPO narratives is partially a spend bubble, not durable SaaS revenue. There's a difference between companies paying for Claude and companies getting value from Claude. Curious if others here are seeing the same pattern - either as users inside companies, or as people following Anthropic's trajectory toward a public offering. submitted by /u/kalabunga_1 [link] [comments]